1. Field
This system provides quantitative methods for summarizing, discriminating, classifying, and/or recognizing shapes and patterns, which will be useful for quality control in the manufacturing of objects, robotic vision, and navigation of vehicles.
2. Prior Art
Machines are known that can distinguish among shapes and patterns, such as for quality control in manufacturing, robotic vision, navigation of vehicles, and registration of differentials of brightness and/or color using a two-dimensional electronic array. The state of each element within the array, such as its level of brightness, is then analyzed by one of several methods. Most often elaborate “segmentation” procedures are used. These break the shape or pattern into its component “features,” which are analyzed individually and then collectively. For an overview, see Image Analysis: Problems, Progress and Prospects, A. Rosenfeld, Pattern Recognition, 17, 3-12 (1984).
U.S. Pat. No. 4,845,764 (1989) to Ueda et al. (1989) and U.S. Pat. No. 5,546,476 (1996) to Mitaka et al. are typical. Each shows a system which recognizes objects by their alignments and spacing in the value pattern that corresponds to the lines and edges of the object. Similarly, U.S. Pat. No. 5,434,803 to Yoshida (1995) shows a system which evaluates the degree of roundness, straightness, and other geometric properties that are present in a given shape, and uses these attributes for recognition. The basic strategy for these systems is to measure and build a summary based on the properties of these lines and edges, such as their length, orientation, and degree of curvature. This kind of processing requires complex and time consuming algorithms. Further, these methods fail if the shape or pattern to be identified provides corrupted or minimal information with respect to those attributes.
A major handicap for development of an effective system has been the general belief that the contour's collinear attributes, i.e., length, orientation and curvature, are the essential defining properties of shapes and patterns. By assuming that they are, it becomes necessary to assess length, orientation and curvature of the major contours to derive an effective summary that can be stored and used for recognition.
However, recent work by me, “Recognition of objects displayed with incomplete sets of discrete boundary dots,” Perceptual and Motor Skills, 104, 1043-1059 (2007), and “Additional evidence that contour attributes are not essential cues for object recognition,” Behavioral and Brain Functions, 2008, 4, 26, has cast doubt on this assumption. My work has shown that humans can recognize a wide range of shapes, e.g., animals, tools, vehicles, and furniture under conditions in which the collinear attributes, such as length, orientation and curvature of the contours, have been severely degraded and are arguably absent. A sparse display of dots that are positioned around the outer boundary of the shape can provide sufficient cues for recognition, and in the works cited I provide evidence that it is even unlikely that the boundary is being mentally reconstructed by connecting the dots.